GPU-Accelerated LOD Generation for Point Clouds (2302.14801v1)

Abstract: About: We introduce a GPU-accelerated LOD construction process that creates a hybrid voxel-point-based variation of the widely used layered point cloud (LPC) structure for LOD rendering and streaming. The massive performance improvements provided by the GPU allow us to improve the quality of lower LODs via color filtering while still increasing construction speed compared to the non-filtered, CPU-based state of the art. Background: LOD structures are required to render hundreds of millions to trillions of points, but constructing them takes time. Results: LOD structures suitable for rendering and streaming are constructed at rates of about 1 billion points per second (with color filtering) to 4 billion points per second (sample-picking/random sampling, state of the art) on an RTX 3090 -- an improvement of a factor of 80 to 400 times over the CPU-based state of the art (12 million points per second). Due to being in-core, model sizes are limited to about 500 million points per 24GB memory. Discussion: Our method currently focuses on maximizing in-core construction speed on the GPU. Issues such as out-of-core construction of arbitrarily large data sets are not addressed, but we expect it to be suitable as a component of bottom-up out-of-core LOD construction schemes.

• The paper's main contribution is a GPU-accelerated LOD generation method that achieves an 80- to 400-fold speedup over traditional CPU-based techniques.
• It employs a hybrid voxel-point structure with advanced color filtering and various sampling strategies to improve LOD quality.
• Results show scalability by processing up to four billion points per second, enabling efficient real-time rendering of massive point clouds.

GPU-Accelerated LOD Generation for Point Clouds Enhances Performance and Quality

Introduction

The paper presents a significant methodological advancement in the field of point cloud processing by introducing a highly performant GPU-accelerated approach for generating Levels of Detail (LOD) structures. It addresses the computational intensity of creating LOD structures necessary for the rendering and streaming of vast point clouds, which can range from hundreds of millions to trillions of points. The proposed method not only accelerates the construction of LOD structures using GPUs but also enhances the quality of lower LODs through color filtering. This approach marks a notable improvement in both construction speed and visual quality over existing CPU-based methods.

Key Contributions

The research introduces a hybrid voxel-point-based variation of layered point clouds and a significant acceleration of LOD structure construction through GPU processing. The core contributions include:

• A hybrid structure that improves upon traditional Layered Point Clouds (LPC) by incorporating color-filtered voxels for lower LODs, aiding in more efficient streaming and rendering due to the higher compression rates of voxel coordinates.
• A GPU-accelerated LOD construction process that significantly outperforms the CPU-based state of the art, achieving an 80 to 400-fold improvement in terms of points processed per second.

Methodology

The methodology revolves around two main processes: splitting the input point cloud data into octree leaf nodes, and subsequently populating inner nodes with voxels that represent a lower-resolution version of their children. This is achieved through:

1. Splitting Input into Leaf Nodes: A hierarchical counting sort approach is utilized, aiming to partition the point cloud into an octree with depth levels, iterating only twice over the entire data set.
2. Voxel Sampling for Inner Nodes: Various strategies, including first-come-first-serve, random, cell-wise average, and neighborhood-weighted average sampling, are explored to downsample points and voxels into coarser representations for inner nodes.

Performance and Quality Evaluation

The paper meticulously evaluates the proposed GPU-accelerated LOD generation approach against a CPU-based state-of-the-art method (Potree). The results demonstrate a dramatic increase in construction speed, with the GPU method processing up to four billion points per second, and improvements in LOD quality through color filtering. The research thoroughly compares different voxel sampling strategies, highlighting the trade-offs between construction performance and visual quality, with neighborhood-weighted averaging offering the best quality improvement.

Implications and Future Directions

The introduction of GPU acceleration into the LOD generation process for point clouds signifies a monumental leap forward, particularly for applications requiring the real-time rendering and streaming of massive point cloud data sets. This method holds promise for various fields, including geospatial analysis, archaeology, and digital heritage preservation, where efficient processing and high-quality rendering of large-scale point clouds are crucial. The paper also opens up avenues for future research, such as exploring out-of-core processing for even larger data sets, improving color filtering techniques, and implementing view-dependent LOD rendering.

The advancements presented in this research hold the potential to significantly impact how large-scale point cloud data is processed, streamed, and visualized, pushing the boundaries of what is possible in real-time rendering applications.